Update README.md

README.md

@@ -16,39 +16,38 @@ metrics:
 I fine-tuned a pre-trained EfficientNet-B1 model to classify images into three categories: pizza, steak, and sushi[cite: 1]. 
 
 ## Model Details
-* **Architecture:** `torchvision.models.efficientnet_b1`[cite: 1]
-* **Weights:** `EfficientNet_B1_Weights.DEFAULT`[cite: 1]
-* **Modifications:** I froze all the base feature layers[cite: 1]. I replaced the classifier head with a `Dropout(p=0.2)` and a `Linear` layer mapping from 1280 in_features to 3 out_features[cite: 1].
-
+* **Architecture:** `torchvision.models.efficientnet_b1`
+* **Weights:** `EfficientNet_B1_Weights.DEFAULT`
+* **Modifications:** I froze all the base feature layers
 ## Training Procedure
-I trained the model for 10 epochs using the Adam optimizer[cite: 1].
+I trained the model for 10 epochs using the Adam optimizer.
 
-* **Batch Size:** 32[cite: 1]
-* **Learning Rate:** 0.001[cite: 1]
-* **Loss Function:** CrossEntropyLoss[cite: 1]
-* **Transforms:** I used the automatic transforms provided by the default EfficientNet-B1 weights[cite: 1]. 
-* **Hardware:** Trained using `cuda` (if available) with a set manual seed of 37 for reproducibility[cite: 1].
+* **Batch Size:** 32
+* **Learning Rate:** 0.001
+* **Loss Function:** CrossEntropyLoss
+* **Transforms:** I used the automatic transforms provided by the default EfficientNet-B1 weights. 
+* **Hardware:** Trained using `cuda` (if available) with a set manual seed of 37 for reproducibility].
 
 ## Dataset
-I used a 20% subset of a pizza, steak, and sushi dataset[cite: 1]. The data was split into `train` and `test` directories[cite: 1]. 
+I used a 20% subset of a pizza, steak, and sushi dataset. The data was split into `train` and `test` directories. 
 
 ## Evaluation Results
 
 ### Accuracy and Loss Curves
-Over the 10 epochs, both the training and testing loss steadily decreased, with the testing loss ending below 0.40[cite: 1]. The testing accuracy outperformed the training accuracy early on and finished highly stable above 90%[cite: 1].
+Over the 10 epochs, both the training and testing loss steadily decreased, with the testing loss ending below 0.40. The testing accuracy outperformed the training accuracy early on and finished highly stable above 90%.
 
 ![Loss and Accuracy Curves](plots/EfficientNet_B1_20percent.pth_curves.png)
 
 ### Confusion Matrix
 The model performs exceptionally well across all three classes on the test set:
-* **Pizza:** 45 correct, 0 misclassified as steak, 1 misclassified as sushi[cite: 1].
-* **Steak:** 56 correct, 0 misclassified as pizza, 2 misclassified as sushi[cite: 1].
-* **Sushi:** 42 correct, 3 misclassified as pizza, 1 misclassified as steak[cite: 1].
+* **Pizza:** 45 correct, 0 misclassified as steak, 1 misclassified as sushi.
+* **Steak:** 56 correct, 0 misclassified as pizza, 2 misclassified as sushi.
+* **Sushi:** 42 correct, 3 misclassified as pizza, 1 misclassified as steak.
 
 ![Confusion Matrix](plots/EfficientNet_B1_20percent.pth_confusion_matrix.png)
 
 ### Most Confident Wrong Predictions
-I plotted the instances where the model was highly confident but incorrect[cite: 1]. The model occasionally struggled with distinguishing close-up textures, such as predicting a steak dish as sushi with 0.82 confidence, or a sushi dish as pizza with 0.61 confidence[cite: 1].
+I plotted the instances where the model was highly confident but incorrect. The model occasionally struggled with distinguishing close-up textures, such as predicting a steak dish as sushi with 0.82 confidence, or a sushi dish as pizza with 0.61 confidence.
 
 ![Wrong Predictions](plots/EfficientNet_B1_20percent.pth_wrong_pred.png)